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MU Jin-Hu, CHEN Yu-Ze, FENG Hui, LI Wen-Jian, ZHOU Li-Bin. A New Revolution in Crop Breeding: The Era of High-Throughput Phenomics[J]. Plant Science Journal, 2016, 34(6): 962-971. DOI: 10.11913/PSJ.2095-0837.2016.60962
Citation: MU Jin-Hu, CHEN Yu-Ze, FENG Hui, LI Wen-Jian, ZHOU Li-Bin. A New Revolution in Crop Breeding: The Era of High-Throughput Phenomics[J]. Plant Science Journal, 2016, 34(6): 962-971. DOI: 10.11913/PSJ.2095-0837.2016.60962
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A New Revolution in Crop Breeding: The Era of High-Throughput Phenomics

  • 1. Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China;

  • 2. University of Chinese Academy of Science, Beijing 100049, China;

  • 3. Gansu Agricultural University, Lanzhou 730070, China

Funds: 

This work was supported by grants from the National Natural Science Foundation of China (11275171, 11205218), Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) (XDA08040111), CAS ‘Light of West China’ Program (29Y506020) and Youth Innovation Promotion Association of CAS (2015337).

More Information
  • Received Date: May 09, 2016
  • Revised Date: May 29, 2016
  • Available Online: October 31, 2022
  • Published Date: December 27, 2016
    Graphical Abstract
    • Abstract
  • Traditional phenomics research has trailed the high-speed development of genomics, transcriptomics, and transcriptomics, thereby restricting crop breeding and functional genomics study. To break this bottleneck, international and domestic researchers have developed various platforms for phenomics analysis with the characteristics of automation, high-precision, and high-throughput, and combined these platforms with numerous ‘omics’ research. These developments will advance a new technology revolution in the research field of crop breeding. In this review, the concepts and significance of plant phenomics are introduced briefly. Analysis on the high-throughput phenomics platform is illustrated in detail. In addition, future development in phenomics and the comprehensive utilization of large biological data are reviewed.
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    • References (76)
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